Abstract Accurate classification of clinically significant (CS) versus clinically insignificant (CiS) prostate cancer is critical for treatment planning, yet clinical adoption of AI-based diagnostic systems remains limited by two fundamental barriers: achieving clinically meaningful performance with balanced sensitivity/specificity for decision support, and lack of transparent decision-making that clinicians can verify and trust. This study addresses both barriers through clinical validation of